In evanodell/cqcr: Access 'Care Quality Commission' Data

knitr::opts_chunk$set( 
  collapse = TRUE,echo = TRUE,
  comment = "#>"
)

library(cqcr)
library(purrr)
library(dplyr)
library(ggplot2)
library(forcats)
library(tidyr)

CQC Partner Codes

The Care Quality Comission does not require registration or the use of keys to access the API, but they do want users to attach a partner code to their requests. If you haven't been provided a code by the CQC, you should use a name or acronym that identifies you or your organisation. You can set the code for each session with cqc_partner_code(), or store it in an environmental variable named CQC_PARTNER_CODE.

Using `cqcr`

Let's look at the care home options available in a few local authorities in East London. The code below retrieves the ID, name and post code of all care homes in Waltham Forest, Hackney and Tower Hamlets local authorities.

loc1 <- cqc_locations_search(care_home = TRUE, 
                             local_authority = c("Waltham Forest",
                                                 "Hackney", "Tower Hamlets"))

Given the list of care home IDs, we can retrieve more detailed information on each of these locations using cqc_location_details(). We can use some functions from purrr and dplyr to create a data frame with some basic information.

care_home_details <- cqc_location_details(loc1)

care_home_df <- map_dfr(care_home_details, `[`,
                        c("location_id", "name", "number_of_beds", 
                          "onspd_longitude", "onspd_latitude"))

care_home_df <- care_home_df %>% 
  mutate(rating = care_home_details %>%
           map(c("current_ratings", "overall", "rating")) %>%
           as.character(),
         rating = recode(rating, "NULL" = "No published rating"),
         rating = factor(rating, levels = c("Outstanding", "Good",
                                            "Requires improvement", 
                                            "Inadequate",
                                            "No published rating")),
         report_date = care_home_details %>%
           map(c("current_ratings", "reportDate")) %>%
           as.character(),
         report_date = ifelse(report_date == "NULL", NA, report_date),
         specialisms = care_home_details %>% map(c("specialisms", "name")))

care_home_df

readr::write_rds(care_home_df, "vignettes/care_home_df.rds")

care_home_df <- readr::read_rds("care_home_df.rds")

care_home_df

Lets take a look at the various specialisms that care homes report. Care homes can report multiple specialisms, so the numbers below add up to more than the number of care homes. I have also used the ratings data to show the number of care homes providing a given specialism with their overall rating.

care_home_df_unnest <- tidyr::unnest(care_home_df, cols = c(specialisms)) %>%
  mutate(specialisms = fct_infreq(specialisms))

theme_set(theme_bw())

p_specialisms_rating <- ggplot(care_home_df_unnest,
                        aes(x = specialisms, fill = rating)) + 
  geom_bar() + 
  scale_x_discrete(labels = scales::wrap_format(33)) + 
  scale_fill_viridis_d(name="", option = "A", end = 0.95) + 
  labs(x = "Specialism(s)", y = "Number of Care Homes") + 
  theme(legend.position = "bottom",
        axis.text.x = element_text(size = 8, angle = 60, hjust = 1))

p_specialisms_rating

Then, we can map our data with the leaflet package. In this case the colours indicate the most recent inspection rating, and the size corresponds to a scaled count of beds.

library(leaflet)

pal <- colorFactor(c("blue", "green", "orange", "red", "gray27"),
                   domain = care_home_df$rating)

labels <- paste0(
  "<strong>Name:</strong> ", care_home_df$name,"</br>",
  "<strong>Number of Beds:</strong> ", care_home_df$number_of_beds, "</br>",
  "<strong>Overall Rating:</strong> ", care_home_df$rating, "</br>",
  "<strong>Report Date:</strong> ", format(as.Date(care_home_df$report_date),
                                           "%e %B %Y"), "</br>",
  "<strong>Specialism(s):</strong><ul><li>",
  lapply(care_home_df$specialisms, paste, collapse='</li><li>'), "</li></ul>"
) %>% lapply(htmltools::HTML)

map <- leaflet(care_home_df) %>%
  addTiles() %>%
    addCircleMarkers(lng = ~onspd_longitude,
             lat = ~onspd_latitude,
             label = labels,
             color = ~pal(rating), 
             radius = ~scales::rescale(care_home_df$number_of_beds, to = c(5, 15)))

map